The Use of Artificial Intelligence (AI) in Teaching English Vocabulary in Oman: Perspectives, Teaching Practices, and Challenges
Bibliographic record
Abstract
Vocabulary plays an outstanding role in the teaching/learning process of second/foreign languages. With recent technological advancements, particularly emerging AI tools, a necessity arises to examine and explore the effect of these AI tools on teaching English. Therefore, this study examines teachers’ attitudes toward using AI tools to teach English vocabulary to EFL Omani students. It also explores their perspectives on the most common AI tools, integration scenarios, and challenges. A mixed-method research design was utilized; the quantitative data included an adopted questionnaire from Alharbi and Khalil (2023) with closed-ended questions, and the qualitative data involved exploratory open-ended questions. Both research designs were distributed randomly to 70 English teachers teaching at the Preparatory Studies Center at one of the Omani universities. The quantitative data were analysed statistically employing SPSS version 29, whereas the qualitative data were analysed thematically. The quantitative data showed that the English instructors have a positive attitude toward the advantages of the use of AI tools in teaching English in general and vocabulary in particular. These quantitative data were supported by their perspectives, revealing that such tools are appropriate and effective since they engage students and increase their learning autonomy. The top five AI tools were ChatGPT, Kahoot, Duolingo, Quizlet, and Google Translate. However, English instructors illustrated some concerns related to lacking familiarity and training in using AI tools, ethical considerations related to the privacy of personal data, shortage of good resources, and enough time since priority is given to cover the course delivery plan. These findings could have pedagogical implications, whereby current textbooks should consider integrating these AI tools to make teaching more effective. Besides, teachers should be provided with sufficient training to exploit such tools in providing their students with effective and engaging teaching methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".